Overview

Dataset statistics

Number of variables24
Number of observations30828
Missing cells64412
Missing cells (%)8.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.5 MiB
Average record size in memory220.3 B

Variable types

Text6
Numeric9
Categorical6
Boolean1
DateTime2

Alerts

width has constant value ""Constant
height has constant value ""Constant
id is highly overall correlated with pub_year and 1 other fieldsHigh correlation
price is highly overall correlated with updated_price and 2 other fieldsHigh correlation
pub_year is highly overall correlated with id and 1 other fieldsHigh correlation
year_of_sale is highly overall correlated with id and 1 other fieldsHigh correlation
updated_price is highly overall correlated with price and 2 other fieldsHigh correlation
edition_num is highly overall correlated with editionHigh correlation
price_range is highly overall correlated with price and 1 other fieldsHigh correlation
edition is highly overall correlated with edition_numHigh correlation
affordability is highly overall correlated with price and 1 other fieldsHigh correlation
edition_num is highly imbalanced (54.7%)Imbalance
availabe is highly imbalanced (93.3%)Imbalance
price_range is highly imbalanced (69.0%)Imbalance
edition is highly imbalanced (54.7%)Imbalance
edition_num has 27974 (90.7%) missing valuesMissing
description has 4232 (13.7%) missing valuesMissing
edition has 27974 (90.7%) missing valuesMissing
Length_of_description has 4232 (13.7%) missing valuesMissing

Reproduction

Analysis started2024-03-17 14:16:19.057578
Analysis finished2024-03-17 14:16:36.637334
Duration17.58 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Title
Text

Distinct26227
Distinct (%)85.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2024-03-17T10:16:36.891840image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Length

Max length248
Median length142
Mean length38.113533
Min length1

Characters and Unicode

Total characters1174964
Distinct characters129
Distinct categories16 ?
Distinct scripts3 ?
Distinct blocks8 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24249 ?
Unique (%)78.7%

Sample

1st rowLearning Go
2nd rowTidy First?
3rd rowHands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
4th rowDesigning Data-Intensive Applications
5th rowExam Ref MS-102 Microsoft 365 Administrator
ValueCountFrequency (%)
and 8757
 
5.6%
in 3946
 
2.5%
for 3677
 
2.3%
the 2844
 
1.8%
of 2653
 
1.7%
with 2286
 
1.5%
systems 2012
 
1.3%
data 1758
 
1.1%
learning 1421
 
0.9%
applications 1386
 
0.9%
Other values (10962) 125972
80.4%
2024-03-17T10:16:37.312963image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
125945
 
10.7%
e 98215
 
8.4%
n 88067
 
7.5%
i 86892
 
7.4%
t 71112
 
6.1%
o 70641
 
6.0%
a 69477
 
5.9%
r 58227
 
5.0%
s 48064
 
4.1%
l 36083
 
3.1%
Other values (119) 422241
35.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 871187
74.1%
Uppercase Letter 147997
 
12.6%
Space Separator 125956
 
10.7%
Decimal Number 15154
 
1.3%
Other Punctuation 7955
 
0.7%
Dash Punctuation 4620
 
0.4%
Open Punctuation 637
 
0.1%
Close Punctuation 634
 
0.1%
Math Symbol 577
 
< 0.1%
Other Symbol 126
 
< 0.1%
Other values (6) 121
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 98215
11.3%
n 88067
10.1%
i 86892
10.0%
t 71112
 
8.2%
o 70641
 
8.1%
a 69477
 
8.0%
r 58227
 
6.7%
s 48064
 
5.5%
l 36083
 
4.1%
c 35308
 
4.1%
Other values (33) 209101
24.0%
Uppercase Letter
ValueCountFrequency (%)
S 16559
11.2%
A 14458
 
9.8%
C 14126
 
9.5%
P 11457
 
7.7%
I 10507
 
7.1%
D 10049
 
6.8%
M 10040
 
6.8%
T 8676
 
5.9%
E 7246
 
4.9%
B 4963
 
3.4%
Other values (22) 39916
27.0%
Other Punctuation
ValueCountFrequency (%)
, 3392
42.6%
: 1672
21.0%
. 1515
19.0%
' 546
 
6.9%
# 254
 
3.2%
& 222
 
2.8%
/ 200
 
2.5%
! 82
 
1.0%
? 39
 
0.5%
* 16
 
0.2%
Other values (5) 17
 
0.2%
Decimal Number
ValueCountFrequency (%)
0 4079
26.9%
2 3881
25.6%
1 2596
17.1%
3 1020
 
6.7%
5 738
 
4.9%
4 690
 
4.6%
6 617
 
4.1%
7 550
 
3.6%
8 532
 
3.5%
9 451
 
3.0%
Math Symbol
ValueCountFrequency (%)
+ 569
98.6%
¬ 3
 
0.5%
3
 
0.5%
1
 
0.2%
1
 
0.2%
Open Punctuation
ValueCountFrequency (%)
( 630
98.9%
[ 4
 
0.6%
2
 
0.3%
1
 
0.2%
Dash Punctuation
ValueCountFrequency (%)
- 4138
89.6%
468
 
10.1%
14
 
0.3%
Other Symbol
ValueCountFrequency (%)
® 109
86.5%
15
 
11.9%
° 2
 
1.6%
Other Number
ValueCountFrequency (%)
½ 2
50.0%
1
25.0%
² 1
25.0%
Space Separator
ValueCountFrequency (%)
125945
> 99.9%
  11
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 630
99.4%
] 4
 
0.6%
Final Punctuation
ValueCountFrequency (%)
92
92.9%
7
 
7.1%
Initial Punctuation
ValueCountFrequency (%)
10
71.4%
4
 
28.6%
Modifier Symbol
ValueCountFrequency (%)
´ 2
100.0%
Format
ValueCountFrequency (%)
­ 1
100.0%
Currency Symbol
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1019183
86.7%
Common 155780
 
13.3%
Cyrillic 1
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 98215
 
9.6%
n 88067
 
8.6%
i 86892
 
8.5%
t 71112
 
7.0%
o 70641
 
6.9%
a 69477
 
6.8%
r 58227
 
5.7%
s 48064
 
4.7%
l 36083
 
3.5%
c 35308
 
3.5%
Other values (64) 357097
35.0%
Common
ValueCountFrequency (%)
125945
80.8%
- 4138
 
2.7%
0 4079
 
2.6%
2 3881
 
2.5%
, 3392
 
2.2%
1 2596
 
1.7%
: 1672
 
1.1%
. 1515
 
1.0%
3 1020
 
0.7%
5 738
 
0.5%
Other values (44) 6804
 
4.4%
Cyrillic
ValueCountFrequency (%)
С 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1173935
99.9%
Punctuation 601
 
0.1%
None 405
 
< 0.1%
Letterlike Symbols 15
 
< 0.1%
Math Operators 5
 
< 0.1%
Currency Symbols 1
 
< 0.1%
Cyrillic 1
 
< 0.1%
Alphabetic PF 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
125945
 
10.7%
e 98215
 
8.4%
n 88067
 
7.5%
i 86892
 
7.4%
t 71112
 
6.1%
o 70641
 
6.0%
a 69477
 
5.9%
r 58227
 
5.0%
s 48064
 
4.1%
l 36083
 
3.1%
Other values (72) 421212
35.9%
Punctuation
ValueCountFrequency (%)
468
77.9%
92
 
15.3%
14
 
2.3%
10
 
1.7%
7
 
1.2%
4
 
0.7%
3
 
0.5%
2
 
0.3%
1
 
0.2%
None
ValueCountFrequency (%)
ü 201
49.6%
® 109
26.9%
ä 22
 
5.4%
  11
 
2.7%
ß 6
 
1.5%
Ü 5
 
1.2%
ö 5
 
1.2%
é 5
 
1.2%
ó 4
 
1.0%
Æ 3
 
0.7%
Other values (21) 34
 
8.4%
Letterlike Symbols
ValueCountFrequency (%)
15
100.0%
Math Operators
ValueCountFrequency (%)
3
60.0%
1
 
20.0%
1
 
20.0%
Currency Symbols
ValueCountFrequency (%)
1
100.0%
Cyrillic
ValueCountFrequency (%)
С 1
100.0%
Alphabetic PF
ValueCountFrequency (%)
1
100.0%

id
Real number (ℝ)

HIGH CORRELATION 

Distinct30819
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90451061
Minimum106017
Maximum2.112314 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-03-17T10:16:37.453964image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum106017
5-th percentile333777.3
Q11316881.8
median95611996
Q32.1003473 × 108
95-th percentile2.107434 × 108
Maximum2.112314 × 108
Range2.1112538 × 108
Interquartile range (IQR)2.0871785 × 108

Descriptive statistics

Standard deviation95145835
Coefficient of variation (CV)1.051904
Kurtosis-1.7238003
Mean90451061
Median Absolute Deviation (MAD)94880937
Skewness0.30935755
Sum2.7884253 × 1012
Variance9.05273 × 1015
MonotonicityNot monotonic
2024-03-17T10:16:37.577968image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
969139 2
 
< 0.1%
2487727 2
 
< 0.1%
2487867 2
 
< 0.1%
1632606 2
 
< 0.1%
211138211 2
 
< 0.1%
2502407 2
 
< 0.1%
2510123 2
 
< 0.1%
209634307 2
 
< 0.1%
210450539 2
 
< 0.1%
211190367 1
 
< 0.1%
Other values (30809) 30809
99.9%
ValueCountFrequency (%)
106017 1
< 0.1%
116047 1
< 0.1%
116386 1
< 0.1%
116387 1
< 0.1%
116991 1
< 0.1%
117479 1
< 0.1%
117483 1
< 0.1%
117501 1
< 0.1%
117536 1
< 0.1%
117736 1
< 0.1%
ValueCountFrequency (%)
211231399 1
< 0.1%
211231398 1
< 0.1%
211231397 1
< 0.1%
211231034 1
< 0.1%
211231021 1
< 0.1%
211231012 1
< 0.1%
211230710 1
< 0.1%
211230697 1
< 0.1%
211230632 1
< 0.1%
211230596 1
< 0.1%

price
Real number (ℝ)

HIGH CORRELATION 

Distinct1307
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.497864
Minimum0
Maximum2353.3694
Zeros108
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-03-17T10:16:37.689965image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile26.9381
Q149.99
median72.641169
Q394.437879
95-th percentile217.95257
Maximum2353.3694
Range2353.3694
Interquartile range (IQR)44.447879

Descriptive statistics

Standard deviation69.133156
Coefficient of variation (CV)0.79011249
Kurtosis75.309387
Mean87.497864
Median Absolute Deviation (MAD)22.651169
Skewness4.7905371
Sum2697384.2
Variance4779.3932
MonotonicityNot monotonic
2024-03-17T10:16:37.794723image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72.64116886 4441
 
14.4%
130.7657289 1796
 
5.8%
49.99 893
 
2.9%
203.4214289 716
 
2.3%
55.99 671
 
2.2%
44.99 669
 
2.2%
83.3021061 502
 
1.6%
63.82076688 467
 
1.5%
77.39285164 440
 
1.4%
76.5005461 419
 
1.4%
Other values (1297) 19814
64.3%
ValueCountFrequency (%)
0 108
0.4%
0.99 1
 
< 0.1%
1.33343595 6
 
< 0.1%
1.99 3
 
< 0.1%
2.68034095 8
 
< 0.1%
2.99 5
 
< 0.1%
3.99 9
 
< 0.1%
4.02724595 7
 
< 0.1%
4.2339711 3
 
< 0.1%
4.74377196 1
 
< 0.1%
ValueCountFrequency (%)
2353.369398 1
< 0.1%
1878.93095 1
< 0.1%
1615.3705 1
< 0.1%
1360.312 1
< 0.1%
1055.97352 1
< 0.1%
935.2145 1
< 0.1%
892.70475 1
< 0.1%
850.195 1
< 0.1%
783.228446 1
< 0.1%
781.2049 1
< 0.1%

author
Text

Distinct20384
Distinct (%)66.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2024-03-17T10:16:38.102381image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Length

Max length106
Median length42
Mean length14.213248
Min length2

Characters and Unicode

Total characters438166
Distinct characters162
Distinct categories17 ?
Distinct scripts3 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15649 ?
Unique (%)50.8%

Sample

1st rowJon Bodner
2nd rowKent Beck
3rd rowAurélien Géron
4th rowMartin Kleppmann
5th rowOrin Thomas
ValueCountFrequency (%)
david 524
 
0.8%
michael 495
 
0.7%
a 426
 
0.6%
j 386
 
0.6%
john 383
 
0.6%
m 380
 
0.6%
r 305
 
0.4%
paul 293
 
0.4%
s 290
 
0.4%
peter 286
 
0.4%
Other values (20622) 64807
94.5%
2024-03-17T10:16:38.533167image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 43807
 
10.0%
37765
 
8.6%
e 34379
 
7.8%
i 28866
 
6.6%
n 28771
 
6.6%
r 27110
 
6.2%
o 22219
 
5.1%
l 17661
 
4.0%
s 15353
 
3.5%
h 14214
 
3.2%
Other values (152) 168021
38.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 323792
73.9%
Uppercase Letter 70732
 
16.1%
Space Separator 37765
 
8.6%
Other Punctuation 4510
 
1.0%
Dash Punctuation 1244
 
0.3%
Close Punctuation 37
 
< 0.1%
Open Punctuation 37
 
< 0.1%
Math Symbol 16
 
< 0.1%
Decimal Number 12
 
< 0.1%
Final Punctuation 11
 
< 0.1%
Other values (7) 10
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 43807
13.5%
e 34379
10.6%
i 28866
 
8.9%
n 28771
 
8.9%
r 27110
 
8.4%
o 22219
 
6.9%
l 17661
 
5.5%
s 15353
 
4.7%
h 14214
 
4.4%
t 14029
 
4.3%
Other values (74) 77383
23.9%
Uppercase Letter
ValueCountFrequency (%)
M 6589
 
9.3%
S 6287
 
8.9%
A 5195
 
7.3%
J 4510
 
6.4%
B 4318
 
6.1%
C 4288
 
6.1%
D 3924
 
5.5%
R 3895
 
5.5%
P 3456
 
4.9%
K 3267
 
4.6%
Other values (38) 25003
35.3%
Decimal Number
ValueCountFrequency (%)
3 4
33.3%
2 3
25.0%
1 2
16.7%
4 1
 
8.3%
9 1
 
8.3%
5 1
 
8.3%
Other Punctuation
ValueCountFrequency (%)
. 4304
95.4%
' 103
 
2.3%
, 89
 
2.0%
" 12
 
0.3%
& 2
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
4
25.0%
4
25.0%
¬ 4
25.0%
3
18.8%
1
 
6.2%
Final Punctuation
ValueCountFrequency (%)
10
90.9%
1
 
9.1%
Format
ValueCountFrequency (%)
1
50.0%
1
50.0%
Space Separator
ValueCountFrequency (%)
37765
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1244
100.0%
Close Punctuation
ValueCountFrequency (%)
) 37
100.0%
Open Punctuation
ValueCountFrequency (%)
( 37
100.0%
Other Symbol
ValueCountFrequency (%)
° 2
100.0%
Nonspacing Mark
ValueCountFrequency (%)
̌ 2
100.0%
Currency Symbol
ValueCountFrequency (%)
£ 1
100.0%
Initial Punctuation
ValueCountFrequency (%)
1
100.0%
Other Number
ValueCountFrequency (%)
² 1
100.0%
Modifier Symbol
ValueCountFrequency (%)
¨ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 394524
90.0%
Common 43639
 
10.0%
Inherited 3
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 43807
 
11.1%
e 34379
 
8.7%
i 28866
 
7.3%
n 28771
 
7.3%
r 27110
 
6.9%
o 22219
 
5.6%
l 17661
 
4.5%
s 15353
 
3.9%
h 14214
 
3.6%
t 14029
 
3.6%
Other values (122) 148115
37.5%
Common
ValueCountFrequency (%)
37765
86.5%
. 4304
 
9.9%
- 1244
 
2.9%
' 103
 
0.2%
, 89
 
0.2%
) 37
 
0.1%
( 37
 
0.1%
" 12
 
< 0.1%
10
 
< 0.1%
4
 
< 0.1%
Other values (18) 34
 
0.1%
Inherited
ValueCountFrequency (%)
̌ 2
66.7%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 436301
99.6%
None 1837
 
0.4%
Punctuation 14
 
< 0.1%
Math Operators 12
 
< 0.1%
Diacriticals 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 43807
 
10.0%
37765
 
8.7%
e 34379
 
7.9%
i 28866
 
6.6%
n 28771
 
6.6%
r 27110
 
6.2%
o 22219
 
5.1%
l 17661
 
4.0%
s 15353
 
3.5%
h 14214
 
3.3%
Other values (57) 166156
38.1%
None
ValueCountFrequency (%)
é 282
15.4%
ü 194
 
10.6%
á 185
 
10.1%
ö 143
 
7.8%
í 143
 
7.8%
ó 104
 
5.7%
ä 81
 
4.4%
ł 60
 
3.3%
Á 43
 
2.3%
ç 42
 
2.3%
Other values (75) 560
30.5%
Punctuation
ValueCountFrequency (%)
10
71.4%
1
 
7.1%
1
 
7.1%
1
 
7.1%
1
 
7.1%
Math Operators
ValueCountFrequency (%)
4
33.3%
4
33.3%
3
25.0%
1
 
8.3%
Diacriticals
ValueCountFrequency (%)
̌ 2
100.0%
Distinct194
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2024-03-17T10:16:38.777219image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Length

Max length68
Median length50
Mean length19.600266
Min length3

Characters and Unicode

Total characters604237
Distinct characters60
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique93 ?
Unique (%)0.3%

Sample

1st rowO'Reilly Media
2nd rowO'Reilly Media
3rd rowO'Reilly Media
4th rowO'Reilly Media
5th rowPearson Education
ValueCountFrequency (%)
publishing 13082
18.5%
springer 10968
15.5%
international 8444
11.9%
packt 3923
 
5.5%
press 3411
 
4.8%
apress 3061
 
4.3%
crc 2719
 
3.8%
berlin 2350
 
3.3%
heidelberg 2350
 
3.3%
education 2145
 
3.0%
Other values (304) 18284
25.8%
2024-03-17T10:16:39.155224image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 65112
 
10.8%
n 60394
 
10.0%
e 50243
 
8.3%
r 48796
 
8.1%
39913
 
6.6%
l 35711
 
5.9%
s 30877
 
5.1%
a 30452
 
5.0%
g 27076
 
4.5%
t 24668
 
4.1%
Other values (50) 190995
31.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 482167
79.8%
Uppercase Letter 80026
 
13.2%
Space Separator 39913
 
6.6%
Other Punctuation 2098
 
0.3%
Dash Punctuation 19
 
< 0.1%
Math Symbol 6
 
< 0.1%
Open Punctuation 4
 
< 0.1%
Close Punctuation 4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 65112
13.5%
n 60394
12.5%
e 50243
10.4%
r 48796
10.1%
l 35711
 
7.4%
s 30877
 
6.4%
a 30452
 
6.3%
g 27076
 
5.6%
t 24668
 
5.1%
b 16001
 
3.3%
Other values (15) 92837
19.3%
Uppercase Letter
ValueCountFrequency (%)
P 22844
28.5%
S 13039
16.3%
I 8629
 
10.8%
C 7236
 
9.0%
R 4481
 
5.6%
E 3463
 
4.3%
A 3196
 
4.0%
H 3156
 
3.9%
B 2615
 
3.3%
W 2606
 
3.3%
Other values (15) 8761
 
10.9%
Other Punctuation
ValueCountFrequency (%)
' 1702
81.1%
& 240
 
11.4%
, 59
 
2.8%
. 53
 
2.5%
! 44
 
2.1%
Space Separator
ValueCountFrequency (%)
39913
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19
100.0%
Math Symbol
ValueCountFrequency (%)
+ 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 562193
93.0%
Common 42044
 
7.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 65112
 
11.6%
n 60394
 
10.7%
e 50243
 
8.9%
r 48796
 
8.7%
l 35711
 
6.4%
s 30877
 
5.5%
a 30452
 
5.4%
g 27076
 
4.8%
t 24668
 
4.4%
P 22844
 
4.1%
Other values (40) 166020
29.5%
Common
ValueCountFrequency (%)
39913
94.9%
' 1702
 
4.0%
& 240
 
0.6%
, 59
 
0.1%
. 53
 
0.1%
! 44
 
0.1%
- 19
 
< 0.1%
+ 6
 
< 0.1%
( 4
 
< 0.1%
) 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 604235
> 99.9%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 65112
 
10.8%
n 60394
 
10.0%
e 50243
 
8.3%
r 48796
 
8.1%
39913
 
6.6%
l 35711
 
5.9%
s 30877
 
5.1%
a 30452
 
5.0%
g 27076
 
4.5%
t 24668
 
4.1%
Other values (49) 190993
31.6%
None
ValueCountFrequency (%)
ä 2
100.0%

pub_year
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2011.6573
Minimum1753
Maximum2025
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-03-17T10:16:39.286438image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1753
5-th percentile2003
Q12012
median2016
Q32020
95-th percentile2023
Maximum2025
Range272
Interquartile range (IQR)8

Descriptive statistics

Standard deviation31.568664
Coefficient of variation (CV)0.015692863
Kurtosis61.051632
Mean2011.6573
Median Absolute Deviation (MAD)4
Skewness-7.8082054
Sum62015372
Variance996.58053
MonotonicityNot monotonic
2024-03-17T10:16:39.399658image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
2022 2350
 
7.6%
2018 2284
 
7.4%
2017 2232
 
7.2%
2016 2080
 
6.7%
2020 2065
 
6.7%
2021 2058
 
6.7%
2019 2047
 
6.6%
2015 1902
 
6.2%
2013 1889
 
6.1%
2014 1760
 
5.7%
Other values (32) 10161
33.0%
ValueCountFrequency (%)
1753 438
1.4%
1980 1
 
< 0.1%
1986 4
 
< 0.1%
1987 1
 
< 0.1%
1988 6
 
< 0.1%
1989 9
 
< 0.1%
1990 12
 
< 0.1%
1991 15
 
< 0.1%
1992 12
 
< 0.1%
1993 22
 
0.1%
ValueCountFrequency (%)
2025 21
 
0.1%
2024 316
 
1.0%
2023 1485
4.8%
2022 2350
7.6%
2021 2058
6.7%
2020 2065
6.7%
2019 2047
6.6%
2018 2284
7.4%
2017 2232
7.2%
2016 2080
6.7%

edition_num
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct21
Distinct (%)0.7%
Missing27974
Missing (%)90.7%
Memory size1.5 MiB
(2nd ed.)
1653 
(3rd ed.)
540 
(4th ed.)
260 
(5th ed.)
 
160
(6th ed.)
 
90
Other values (16)
 
151

Length

Max length11
Median length10
Mean length10.015767
Min length10

Characters and Unicode

Total characters28585
Distinct characters21
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.2%

Sample

1st row (2nd ed.)
2nd row (3rd ed.)
3rd row (8th ed.)
4th row (5th ed.)
5th row (2nd ed.)

Common Values

ValueCountFrequency (%)
(2nd ed.) 1653
 
5.4%
(3rd ed.) 540
 
1.8%
(4th ed.) 260
 
0.8%
(5th ed.) 160
 
0.5%
(6th ed.) 90
 
0.3%
(7th ed.) 54
 
0.2%
(8th ed.) 32
 
0.1%
(9th ed.) 20
 
0.1%
(10th ed.) 14
 
< 0.1%
(11th ed.) 7
 
< 0.1%
Other values (11) 24
 
0.1%
(Missing) 27974
90.7%

Length

2024-03-17T10:16:39.497836image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ed 2854
50.0%
2nd 1653
29.0%
3rd 540
 
9.5%
4th 260
 
4.6%
5th 160
 
2.8%
6th 90
 
1.6%
7th 54
 
0.9%
8th 32
 
0.6%
9th 20
 
0.4%
10th 14
 
0.2%
Other values (12) 31
 
0.5%

Most occurring characters

ValueCountFrequency (%)
5708
20.0%
d 5048
17.7%
e 2854
10.0%
. 2854
10.0%
) 2854
10.0%
( 2854
10.0%
2 1663
 
5.8%
n 1654
 
5.8%
t 660
 
2.3%
h 658
 
2.3%
Other values (11) 1778
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 11416
39.9%
Space Separator 5708
20.0%
Decimal Number 2899
 
10.1%
Other Punctuation 2854
 
10.0%
Close Punctuation 2854
 
10.0%
Open Punctuation 2854
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1663
57.4%
3 543
 
18.7%
4 265
 
9.1%
5 163
 
5.6%
6 91
 
3.1%
7 55
 
1.9%
1 50
 
1.7%
8 33
 
1.1%
9 22
 
0.8%
0 14
 
0.5%
Lowercase Letter
ValueCountFrequency (%)
d 5048
44.2%
e 2854
25.0%
n 1654
 
14.5%
t 660
 
5.8%
h 658
 
5.8%
r 540
 
4.7%
s 2
 
< 0.1%
Space Separator
ValueCountFrequency (%)
5708
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2854
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2854
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2854
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 17169
60.1%
Latin 11416
39.9%

Most frequent character per script

Common
ValueCountFrequency (%)
5708
33.2%
. 2854
16.6%
) 2854
16.6%
( 2854
16.6%
2 1663
 
9.7%
3 543
 
3.2%
4 265
 
1.5%
5 163
 
0.9%
6 91
 
0.5%
7 55
 
0.3%
Other values (4) 119
 
0.7%
Latin
ValueCountFrequency (%)
d 5048
44.2%
e 2854
25.0%
n 1654
 
14.5%
t 660
 
5.8%
h 658
 
5.8%
r 540
 
4.7%
s 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28585
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5708
20.0%
d 5048
17.7%
e 2854
10.0%
. 2854
10.0%
) 2854
10.0%
( 2854
10.0%
2 1663
 
5.8%
n 1654
 
5.8%
t 660
 
2.3%
h 658
 
2.3%
Other values (11) 1778
 
6.2%

description
Text

MISSING 

Distinct25729
Distinct (%)96.7%
Missing4232
Missing (%)13.7%
Memory size1.5 MiB
2024-03-17T10:16:39.801942image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Length

Max length350
Median length344
Mean length266.82223
Min length6

Characters and Unicode

Total characters7096404
Distinct characters187
Distinct categories18 ?
Distinct scripts3 ?
Distinct blocks9 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25139 ?
Unique (%)94.5%

Sample

1st rowGo has rapidly become the preferred language for building web services. Plenty of tutorials are available to teach Go's syntax to developers with experience in other programming languages, but tutorials aren't enough. They don't teach Go's idioms, so developers end up recreating patterns that don't make sense in a Go context. This practical...
2nd rowTidying up messy software is a must. And that means breaking up the code to make it more readable, and using guard clauses and helping functions to make it understandable. In this practical guide, author Kent Beck, creator of Extreme Programming and pioneer of software patterns, suggests when and where you might apply tidyings in your code. ...
3rd rowThrough a recent series of breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This bestselling book uses concrete examples, minimal theory, and production-ready...
4th rowData is at the center of many challenges in system design today. Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What...
5th rowPrepare for Microsoft Exam MS-102 and help demonstrate your real-world mastery of skills and knowledge required to deploy and manage Microsoft 365 and perform Microsoft 365 tenant-level implementation and administration of cloud and hybrid...
ValueCountFrequency (%)
the 57597
 
5.5%
and 43430
 
4.2%
of 34273
 
3.3%
in 26279
 
2.5%
to 23825
 
2.3%
this 17961
 
1.7%
a 16551
 
1.6%
book 13242
 
1.3%
on 12187
 
1.2%
for 11113
 
1.1%
Other values (42743) 781576
75.3%
2024-03-17T10:16:40.279780image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1026204
14.5%
e 683046
 
9.6%
t 462942
 
6.5%
n 442988
 
6.2%
o 439771
 
6.2%
i 427951
 
6.0%
a 401346
 
5.7%
s 369472
 
5.2%
r 346435
 
4.9%
l 220716
 
3.1%
Other values (177) 2275533
32.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5445507
76.7%
Space Separator 1027546
 
14.5%
Uppercase Letter 280785
 
4.0%
Other Punctuation 203869
 
2.9%
Decimal Number 99567
 
1.4%
Dash Punctuation 24978
 
0.4%
Open Punctuation 4398
 
0.1%
Close Punctuation 4257
 
0.1%
Final Punctuation 3285
 
< 0.1%
Math Symbol 1083
 
< 0.1%
Other values (8) 1129
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 683046
12.5%
t 462942
 
8.5%
n 442988
 
8.1%
o 439771
 
8.1%
i 427951
 
7.9%
a 401346
 
7.4%
s 369472
 
6.8%
r 346435
 
6.4%
l 220716
 
4.1%
c 208089
 
3.8%
Other values (58) 1442751
26.5%
Uppercase Letter
ValueCountFrequency (%)
T 36569
13.0%
I 29263
 
10.4%
S 26992
 
9.6%
C 26524
 
9.4%
A 22778
 
8.1%
P 15160
 
5.4%
M 13590
 
4.8%
D 12418
 
4.4%
E 11596
 
4.1%
L 9243
 
3.3%
Other values (28) 76652
27.3%
Other Punctuation
ValueCountFrequency (%)
. 107568
52.8%
, 71021
34.8%
; 6583
 
3.2%
& 6103
 
3.0%
' 3445
 
1.7%
: 2682
 
1.3%
" 1425
 
0.7%
? 1408
 
0.7%
/ 1290
 
0.6%
! 873
 
0.4%
Other values (11) 1471
 
0.7%
Decimal Number
ValueCountFrequency (%)
2 24155
24.3%
1 22112
22.2%
0 20480
20.6%
3 5436
 
5.5%
9 4990
 
5.0%
5 4799
 
4.8%
4 4661
 
4.7%
6 4482
 
4.5%
8 4291
 
4.3%
7 4161
 
4.2%
Math Symbol
ValueCountFrequency (%)
+ 1048
96.8%
> 17
 
1.6%
= 4
 
0.4%
4
 
0.4%
< 4
 
0.4%
| 2
 
0.2%
~ 2
 
0.2%
1
 
0.1%
1
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 23021
92.2%
1092
 
4.4%
854
 
3.4%
5
 
< 0.1%
5
 
< 0.1%
1
 
< 0.1%
Other Symbol
ValueCountFrequency (%)
® 276
72.8%
59
 
15.6%
© 17
 
4.5%
° 14
 
3.7%
12
 
3.2%
1
 
0.3%
Space Separator
ValueCountFrequency (%)
1026204
99.9%
  1309
 
0.1%
  31
 
< 0.1%
2
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 4337
98.6%
31
 
0.7%
[ 26
 
0.6%
{ 4
 
0.1%
Close Punctuation
ValueCountFrequency (%)
) 4228
99.3%
] 26
 
0.6%
} 3
 
0.1%
Final Punctuation
ValueCountFrequency (%)
2942
89.6%
339
 
10.3%
» 4
 
0.1%
Initial Punctuation
ValueCountFrequency (%)
445
85.1%
74
 
14.1%
« 4
 
0.8%
Modifier Symbol
ValueCountFrequency (%)
` 12
70.6%
´ 4
 
23.5%
˜ 1
 
5.9%
Other Number
ValueCountFrequency (%)
² 4
57.1%
½ 2
28.6%
¾ 1
 
14.3%
Format
ValueCountFrequency (%)
­ 81
51.9%
75
48.1%
Currency Symbol
ValueCountFrequency (%)
$ 30
78.9%
¢ 8
 
21.1%
Connector Punctuation
ValueCountFrequency (%)
_ 7
100.0%
Control
ValueCountFrequency (%)
— 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5726284
80.7%
Common 1370117
 
19.3%
Greek 3
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 683046
 
11.9%
t 462942
 
8.1%
n 442988
 
7.7%
o 439771
 
7.7%
i 427951
 
7.5%
a 401346
 
7.0%
s 369472
 
6.5%
r 346435
 
6.0%
l 220716
 
3.9%
c 208089
 
3.6%
Other values (91) 1723528
30.1%
Common
ValueCountFrequency (%)
1026204
74.9%
. 107568
 
7.9%
, 71021
 
5.2%
2 24155
 
1.8%
- 23021
 
1.7%
1 22112
 
1.6%
0 20480
 
1.5%
; 6583
 
0.5%
& 6103
 
0.4%
3 5436
 
0.4%
Other values (73) 57434
 
4.2%
Greek
ValueCountFrequency (%)
Ω 1
33.3%
λ 1
33.3%
α 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7086666
99.9%
Punctuation 5959
 
0.1%
None 3679
 
0.1%
Letterlike Symbols 63
 
< 0.1%
Alphabetic PF 17
 
< 0.1%
Specials 12
 
< 0.1%
Math Operators 6
 
< 0.1%
Box Drawing 1
 
< 0.1%
Modifier Letters 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1026204
14.5%
e 683046
 
9.6%
t 462942
 
6.5%
n 442988
 
6.3%
o 439771
 
6.2%
i 427951
 
6.0%
a 401346
 
5.7%
s 369472
 
5.2%
r 346435
 
4.9%
l 220716
 
3.1%
Other values (84) 2265795
32.0%
Punctuation
ValueCountFrequency (%)
2942
49.4%
1092
 
18.3%
854
 
14.3%
445
 
7.5%
339
 
5.7%
75
 
1.3%
74
 
1.2%
50
 
0.8%
42
 
0.7%
31
 
0.5%
Other values (5) 15
 
0.3%
None
ValueCountFrequency (%)
  1309
35.6%
ü 690
18.8%
ä 495
 
13.5%
® 276
 
7.5%
ö 228
 
6.2%
­ 81
 
2.2%
ß 81
 
2.2%
Ü 53
 
1.4%
é 51
 
1.4%
â 40
 
1.1%
Other values (57) 375
 
10.2%
Letterlike Symbols
ValueCountFrequency (%)
59
93.7%
4
 
6.3%
Specials
ValueCountFrequency (%)
12
100.0%
Alphabetic PF
ValueCountFrequency (%)
10
58.8%
6
35.3%
1
 
5.9%
Math Operators
ValueCountFrequency (%)
4
66.7%
1
 
16.7%
1
 
16.7%
Box Drawing
ValueCountFrequency (%)
1
100.0%
Modifier Letters
ValueCountFrequency (%)
˜ 1
100.0%

availabe
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
True
30583 
False
 
245
ValueCountFrequency (%)
True 30583
99.2%
False 245
 
0.8%
2024-03-17T10:16:40.408832image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Distinct6476
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
Minimum1753-01-01 00:00:00
Maximum2025-02-01 00:00:00
2024-03-17T10:16:40.502542image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:40.790141image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct425
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
Minimum1753-01-01 00:00:00
Maximum2025-02-01 00:00:00
2024-03-17T10:16:40.929177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:41.045326image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

num_of_author
Real number (ℝ)

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2194758
Minimum1
Maximum59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-03-17T10:16:41.142273image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile5
Maximum59
Range58
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7157742
Coefficient of variation (CV)0.77305382
Kurtosis53.04646
Mean2.2194758
Median Absolute Deviation (MAD)1
Skewness3.8186391
Sum68422
Variance2.9438812
MonotonicityNot monotonic
2024-03-17T10:16:41.233283image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 13849
44.9%
2 7472
24.2%
3 4407
 
14.3%
4 2554
 
8.3%
5 1264
 
4.1%
6 576
 
1.9%
7 247
 
0.8%
8 170
 
0.6%
10 82
 
0.3%
9 81
 
0.3%
Other values (14) 126
 
0.4%
ValueCountFrequency (%)
1 13849
44.9%
2 7472
24.2%
3 4407
 
14.3%
4 2554
 
8.3%
5 1264
 
4.1%
6 576
 
1.9%
7 247
 
0.8%
8 170
 
0.6%
9 81
 
0.3%
10 82
 
0.3%
ValueCountFrequency (%)
59 1
 
< 0.1%
30 1
 
< 0.1%
24 1
 
< 0.1%
22 1
 
< 0.1%
20 3
 
< 0.1%
19 2
 
< 0.1%
18 3
 
< 0.1%
17 9
< 0.1%
16 8
< 0.1%
15 10
< 0.1%

width
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
97
30828 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters61656
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row97
2nd row97
3rd row97
4th row97
5th row97

Common Values

ValueCountFrequency (%)
97 30828
100.0%

Length

2024-03-17T10:16:41.336287image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-17T10:16:41.434880image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
97 30828
100.0%

Most occurring characters

ValueCountFrequency (%)
9 30828
50.0%
7 30828
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 61656
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 30828
50.0%
7 30828
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 61656
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 30828
50.0%
7 30828
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 61656
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 30828
50.0%
7 30828
50.0%

height
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
150
30828 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters92484
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row150
2nd row150
3rd row150
4th row150
5th row150

Common Values

ValueCountFrequency (%)
150 30828
100.0%

Length

2024-03-17T10:16:41.500879image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-17T10:16:41.580910image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
150 30828
100.0%

Most occurring characters

ValueCountFrequency (%)
1 30828
33.3%
5 30828
33.3%
0 30828
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 92484
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 30828
33.3%
5 30828
33.3%
0 30828
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 92484
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 30828
33.3%
5 30828
33.3%
0 30828
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 92484
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 30828
33.3%
5 30828
33.3%
0 30828
33.3%
Distinct24823
Distinct (%)80.5%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2024-03-17T10:16:41.807879image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Length

Max length228
Median length137
Mean length37.017354
Min length0

Characters and Unicode

Total characters1141171
Distinct characters49
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22210 ?
Unique (%)72.0%

Sample

1st rowlearning go
2nd rowtidy first
3rd rowhands on machine learning with scikit learn keras and tensorflow
4th rowdesigning data intensive applications
5th rowexam ref ms microsoft administrator
ValueCountFrequency (%)
and 8760
 
5.7%
in 4103
 
2.7%
for 3677
 
2.4%
the 2851
 
1.9%
of 2662
 
1.7%
with 2286
 
1.5%
systems 2027
 
1.3%
data 1816
 
1.2%
learning 1454
 
1.0%
applications 1387
 
0.9%
Other values (9281) 121385
79.6%
2024-03-17T10:16:42.228650image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
125213
11.0%
e 105323
 
9.2%
i 97278
 
8.5%
n 91635
 
8.0%
a 83097
 
7.3%
t 79748
 
7.0%
o 74501
 
6.5%
s 64065
 
5.6%
r 62783
 
5.5%
c 48829
 
4.3%
Other values (39) 308699
27.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1015954
89.0%
Space Separator 125213
 
11.0%
Other Number 4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 105323
 
10.4%
i 97278
 
9.6%
n 91635
 
9.0%
a 83097
 
8.2%
t 79748
 
7.8%
o 74501
 
7.3%
s 64065
 
6.3%
r 62783
 
6.2%
c 48829
 
4.8%
l 41038
 
4.0%
Other values (35) 267657
26.3%
Other Number
ValueCountFrequency (%)
½ 2
50.0%
² 1
25.0%
1
25.0%
Space Separator
ValueCountFrequency (%)
125213
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1015953
89.0%
Common 125217
 
11.0%
Cyrillic 1
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 105323
 
10.4%
i 97278
 
9.6%
n 91635
 
9.0%
a 83097
 
8.2%
t 79748
 
7.8%
o 74501
 
7.3%
s 64065
 
6.3%
r 62783
 
6.2%
c 48829
 
4.8%
l 41038
 
4.0%
Other values (34) 267656
26.3%
Common
ValueCountFrequency (%)
125213
> 99.9%
½ 2
 
< 0.1%
² 1
 
< 0.1%
1
 
< 0.1%
Cyrillic
ValueCountFrequency (%)
с 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1140893
> 99.9%
None 276
 
< 0.1%
Alphabetic PF 1
 
< 0.1%
Cyrillic 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
125213
11.0%
e 105323
 
9.2%
i 97278
 
8.5%
n 91635
 
8.0%
a 83097
 
7.3%
t 79748
 
7.0%
o 74501
 
6.5%
s 64065
 
5.6%
r 62783
 
5.5%
c 48829
 
4.3%
Other values (17) 308421
27.0%
None
ValueCountFrequency (%)
ü 206
74.6%
ä 24
 
8.7%
ö 8
 
2.9%
é 7
 
2.5%
ß 6
 
2.2%
ó 4
 
1.4%
æ 3
 
1.1%
ç 2
 
0.7%
ł 2
 
0.7%
ã 2
 
0.7%
Other values (10) 12
 
4.3%
Alphabetic PF
ValueCountFrequency (%)
1
100.0%
Cyrillic
ValueCountFrequency (%)
с 1
100.0%
Distinct25520
Distinct (%)82.8%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2024-03-17T10:16:42.527619image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Length

Max length328
Median length288
Mean length170.08122
Min length3

Characters and Unicode

Total characters5243264
Distinct characters76
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24800 ?
Unique (%)80.4%

Sample

1st rowgo rapidly become preferred language building web services plenty tutorials available teach go syntax developers experience programming languages tutorials enough teach go idioms developers end recreating patterns make sense go context practical
2nd rowtidying messy software means breaking code make readable using guard clauses helping functions make understandable practical guide author kent beck creator extreme programming pioneer software patterns suggests might apply tidyings code
3rd rowrecent series breakthroughs deep learning boosted entire field machine learning even programmers know close nothing technology use simple efficient tools implement programs capable learning data bestselling book uses concrete examples minimal theory production ready
4th rowdata center many challenges system design today difficult issues need figured scalability consistency reliability efficiency maintainability addition overwhelming variety tools including relational databases nosql datastores stream batch processors message brokers
5th rowprepare microsoft exam ms help demonstrate real world mastery skills knowledge required deploy manage microsoft perform microsoft tenant level implementation administration cloud hybrid
ValueCountFrequency (%)
book 13426
 
2.1%
conference 6090
 
0.9%
held 5634
 
0.9%
international 5477
 
0.8%
papers 5104
 
0.8%
proceedings 5007
 
0.8%
constitutes 4834
 
0.7%
data 4648
 
0.7%
th 4504
 
0.7%
nan 4232
 
0.6%
Other values (29465) 593601
91.0%
2024-03-17T10:16:42.970705image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
621729
11.9%
e 592243
11.3%
i 374708
 
7.1%
n 356834
 
6.8%
s 344880
 
6.6%
t 342768
 
6.5%
a 330071
 
6.3%
o 316673
 
6.0%
r 314096
 
6.0%
c 226340
 
4.3%
Other values (66) 1422922
27.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4621517
88.1%
Space Separator 621729
 
11.9%
Connector Punctuation 7
 
< 0.1%
Other Number 7
 
< 0.1%
Uppercase Letter 4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 592243
12.8%
i 374708
 
8.1%
n 356834
 
7.7%
s 344880
 
7.5%
t 342768
 
7.4%
a 330071
 
7.1%
o 316673
 
6.9%
r 314096
 
6.8%
c 226340
 
4.9%
l 214184
 
4.6%
Other values (60) 1208720
26.2%
Other Number
ValueCountFrequency (%)
² 4
57.1%
½ 2
28.6%
¾ 1
 
14.3%
Space Separator
ValueCountFrequency (%)
621729
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 7
100.0%
Uppercase Letter
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4621513
88.1%
Common 621748
 
11.9%
Greek 3
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 592243
12.8%
i 374708
 
8.1%
n 356834
 
7.7%
s 344880
 
7.5%
t 342768
 
7.4%
a 330071
 
7.1%
o 316673
 
6.9%
r 314096
 
6.8%
c 226340
 
4.9%
l 214184
 
4.6%
Other values (56) 1208716
26.2%
Common
ValueCountFrequency (%)
621729
> 99.9%
_ 7
 
< 0.1%
² 4
 
< 0.1%
4
 
< 0.1%
½ 2
 
< 0.1%
µ 1
 
< 0.1%
¾ 1
 
< 0.1%
Greek
ValueCountFrequency (%)
α 1
33.3%
λ 1
33.3%
ω 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5241346
> 99.9%
None 1897
 
< 0.1%
Alphabetic PF 17
 
< 0.1%
Letterlike Symbols 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
621729
11.9%
e 592243
11.3%
i 374708
 
7.1%
n 356834
 
6.8%
s 344880
 
6.6%
t 342768
 
6.5%
a 330071
 
6.3%
o 316673
 
6.0%
r 314096
 
6.0%
c 226340
 
4.3%
Other values (18) 1421004
27.1%
None
ValueCountFrequency (%)
ü 743
39.2%
ä 497
26.2%
ö 231
 
12.2%
ß 81
 
4.3%
é 57
 
3.0%
â 40
 
2.1%
á 39
 
2.1%
ó 39
 
2.1%
ã 30
 
1.6%
í 18
 
0.9%
Other values (34) 122
 
6.4%
Alphabetic PF
ValueCountFrequency (%)
10
58.8%
6
35.3%
1
 
5.9%
Letterlike Symbols
ValueCountFrequency (%)
4
100.0%

price_range
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0-100
23575 
101-500
7206 
501-1000
 
42
1001-1500
 
2
1501-2000
 
2

Length

Max length9
Median length5
Mean length5.472233
Min length5

Characters and Unicode

Total characters168698
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0-100
2nd row0-100
3rd row0-100
4th row0-100
5th row0-100

Common Values

ValueCountFrequency (%)
0-100 23575
76.5%
101-500 7206
 
23.4%
501-1000 42
 
0.1%
1001-1500 2
 
< 0.1%
1501-2000 2
 
< 0.1%
2001-2500 1
 
< 0.1%

Length

2024-03-17T10:16:43.111703image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-17T10:16:43.213702image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0-100 23575
76.5%
101-500 7206
 
23.4%
501-1000 42
 
0.1%
1001-1500 2
 
< 0.1%
1501-2000 2
 
< 0.1%
2001-2500 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 92531
54.9%
1 38082
22.6%
- 30828
 
18.3%
5 7253
 
4.3%
2 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 137870
81.7%
Dash Punctuation 30828
 
18.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 92531
67.1%
1 38082
27.6%
5 7253
 
5.3%
2 4
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 30828
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 168698
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 92531
54.9%
1 38082
22.6%
- 30828
 
18.3%
5 7253
 
4.3%
2 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 168698
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 92531
54.9%
1 38082
22.6%
- 30828
 
18.3%
5 7253
 
4.3%
2 4
 
< 0.1%

month_of_sale
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9017452
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-03-17T10:16:43.297460image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4385711
Coefficient of variation (CV)0.49821762
Kurtosis-1.1936179
Mean6.9017452
Median Absolute Deviation (MAD)3
Skewness-0.14277669
Sum212767
Variance11.823771
MonotonicityNot monotonic
2024-03-17T10:16:43.373474image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
11 3122
10.1%
12 2998
9.7%
9 2847
9.2%
10 2797
9.1%
6 2618
8.5%
8 2604
8.4%
7 2544
8.3%
3 2538
8.2%
4 2319
7.5%
5 2308
7.5%
Other values (2) 4133
13.4%
ValueCountFrequency (%)
1 2188
7.1%
2 1945
6.3%
3 2538
8.2%
4 2319
7.5%
5 2308
7.5%
6 2618
8.5%
7 2544
8.3%
8 2604
8.4%
9 2847
9.2%
10 2797
9.1%
ValueCountFrequency (%)
12 2998
9.7%
11 3122
10.1%
10 2797
9.1%
9 2847
9.2%
8 2604
8.4%
7 2544
8.3%
6 2618
8.5%
5 2308
7.5%
4 2319
7.5%
3 2538
8.2%

date_of_sale
Real number (ℝ)

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.477845
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-03-17T10:16:43.461483image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median17
Q325
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)17

Descriptive statistics

Standard deviation9.1850705
Coefficient of variation (CV)0.55741941
Kurtosis-1.2400813
Mean16.477845
Median Absolute Deviation (MAD)8
Skewness-0.071273009
Sum507979
Variance84.365519
MonotonicityNot monotonic
2024-03-17T10:16:43.586212image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 1328
 
4.3%
30 1300
 
4.2%
28 1262
 
4.1%
29 1156
 
3.7%
19 1098
 
3.6%
31 1098
 
3.6%
25 1053
 
3.4%
6 1043
 
3.4%
27 1035
 
3.4%
15 1021
 
3.3%
Other values (21) 19434
63.0%
ValueCountFrequency (%)
1 1328
4.3%
2 836
2.7%
3 951
3.1%
4 812
2.6%
5 865
2.8%
6 1043
3.4%
7 931
3.0%
8 985
3.2%
9 960
3.1%
10 937
3.0%
ValueCountFrequency (%)
31 1098
3.6%
30 1300
4.2%
29 1156
3.7%
28 1262
4.1%
27 1035
3.4%
26 1010
3.3%
25 1053
3.4%
24 1006
3.3%
23 926
3.0%
22 1015
3.3%

year_of_sale
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2015.3269
Minimum1753
Maximum2025
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-03-17T10:16:43.688216image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1753
5-th percentile2005
Q12012
median2016
Q32020
95-th percentile2023
Maximum2025
Range272
Interquartile range (IQR)8

Descriptive statistics

Standard deviation7.0266161
Coefficient of variation (CV)0.0034865887
Kurtosis502.80242
Mean2015.3269
Median Absolute Deviation (MAD)4
Skewness-13.936303
Sum62128499
Variance49.373333
MonotonicityNot monotonic
2024-03-17T10:16:43.800216image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
2022 2354
 
7.6%
2018 2323
 
7.5%
2017 2234
 
7.2%
2016 2090
 
6.8%
2020 2068
 
6.7%
2021 2058
 
6.7%
2019 2047
 
6.6%
2013 1983
 
6.4%
2015 1914
 
6.2%
2014 1804
 
5.9%
Other values (32) 9953
32.3%
ValueCountFrequency (%)
1753 8
 
< 0.1%
1980 1
 
< 0.1%
1986 4
 
< 0.1%
1987 1
 
< 0.1%
1988 5
 
< 0.1%
1989 10
< 0.1%
1990 12
< 0.1%
1991 15
< 0.1%
1992 11
< 0.1%
1993 20
0.1%
ValueCountFrequency (%)
2025 21
 
0.1%
2024 316
 
1.0%
2023 1488
4.8%
2022 2354
7.6%
2021 2058
6.7%
2020 2068
6.7%
2019 2047
6.6%
2018 2323
7.5%
2017 2234
7.2%
2016 2090
6.8%

updated_price
Real number (ℝ)

HIGH CORRELATION 

Distinct1274
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.497879
Minimum0
Maximum2353.37
Zeros108
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-03-17T10:16:43.928303image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile26.94
Q149.99
median72.64
Q394.44
95-th percentile217.95
Maximum2353.37
Range2353.37
Interquartile range (IQR)44.45

Descriptive statistics

Standard deviation69.133299
Coefficient of variation (CV)0.790114
Kurtosis75.308757
Mean87.497879
Median Absolute Deviation (MAD)22.65
Skewness4.7905115
Sum2697384.6
Variance4779.413
MonotonicityNot monotonic
2024-03-17T10:16:44.059342image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72.64 4441
 
14.4%
130.77 1796
 
5.8%
49.99 895
 
2.9%
203.42 716
 
2.3%
55.99 671
 
2.2%
44.99 669
 
2.2%
83.3 502
 
1.6%
63.82 467
 
1.5%
77.39 440
 
1.4%
76.5 419
 
1.4%
Other values (1264) 19812
64.3%
ValueCountFrequency (%)
0 108
0.4%
0.99 1
 
< 0.1%
1.33 6
 
< 0.1%
1.99 3
 
< 0.1%
2.68 8
 
< 0.1%
2.99 5
 
< 0.1%
3.99 9
 
< 0.1%
4.03 7
 
< 0.1%
4.23 3
 
< 0.1%
4.74 1
 
< 0.1%
ValueCountFrequency (%)
2353.37 1
< 0.1%
1878.93 1
< 0.1%
1615.37 1
< 0.1%
1360.31 1
< 0.1%
1055.97 1
< 0.1%
935.21 1
< 0.1%
892.7 1
< 0.1%
850.2 1
< 0.1%
783.23 1
< 0.1%
781.2 1
< 0.1%

edition
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct21
Distinct (%)0.7%
Missing27974
Missing (%)90.7%
Memory size1.5 MiB
2
1653 
3
540 
4
260 
5
 
160
6
 
90
Other values (16)
 
151

Length

Max length2
Median length1
Mean length1.0157673
Min length1

Characters and Unicode

Total characters2899
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.2%

Sample

1st row2
2nd row3
3rd row8
4th row5
5th row2

Common Values

ValueCountFrequency (%)
2 1653
 
5.4%
3 540
 
1.8%
4 260
 
0.8%
5 160
 
0.5%
6 90
 
0.3%
7 54
 
0.2%
8 32
 
0.1%
9 20
 
0.1%
10 14
 
< 0.1%
11 7
 
< 0.1%
Other values (11) 24
 
0.1%
(Missing) 27974
90.7%

Length

2024-03-17T10:16:44.157018image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2 1653
57.9%
3 540
 
18.9%
4 260
 
9.1%
5 160
 
5.6%
6 90
 
3.2%
7 54
 
1.9%
8 32
 
1.1%
9 20
 
0.7%
10 14
 
0.5%
12 7
 
0.2%
Other values (11) 24
 
0.8%

Most occurring characters

ValueCountFrequency (%)
2 1663
57.4%
3 543
 
18.7%
4 265
 
9.1%
5 163
 
5.6%
6 91
 
3.1%
7 55
 
1.9%
1 50
 
1.7%
8 33
 
1.1%
9 22
 
0.8%
0 14
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2899
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1663
57.4%
3 543
 
18.7%
4 265
 
9.1%
5 163
 
5.6%
6 91
 
3.1%
7 55
 
1.9%
1 50
 
1.7%
8 33
 
1.1%
9 22
 
0.8%
0 14
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 2899
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 1663
57.4%
3 543
 
18.7%
4 265
 
9.1%
5 163
 
5.6%
6 91
 
3.1%
7 55
 
1.9%
1 50
 
1.7%
8 33
 
1.1%
9 22
 
0.8%
0 14
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2899
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 1663
57.4%
3 543
 
18.7%
4 265
 
9.1%
5 163
 
5.6%
6 91
 
3.1%
7 55
 
1.9%
1 50
 
1.7%
8 33
 
1.1%
9 22
 
0.8%
0 14
 
0.5%

affordability
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
Average price
18608 
Low price
11317 
Over priced
 
903

Length

Max length13
Median length13
Mean length11.473012
Min length9

Characters and Unicode

Total characters353690
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAverage price
2nd rowLow price
3rd rowAverage price
4th rowAverage price
5th rowLow price

Common Values

ValueCountFrequency (%)
Average price 18608
60.4%
Low price 11317
36.7%
Over priced 903
 
2.9%

Length

2024-03-17T10:16:44.246166image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-17T10:16:44.349212image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
price 29925
48.5%
average 18608
30.2%
low 11317
 
18.4%
over 903
 
1.5%
priced 903
 
1.5%

Most occurring characters

ValueCountFrequency (%)
e 68947
19.5%
r 50339
14.2%
30828
8.7%
p 30828
8.7%
i 30828
8.7%
c 30828
8.7%
v 19511
 
5.5%
A 18608
 
5.3%
a 18608
 
5.3%
g 18608
 
5.3%
Other values (5) 35757
10.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 292034
82.6%
Space Separator 30828
 
8.7%
Uppercase Letter 30828
 
8.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 68947
23.6%
r 50339
17.2%
p 30828
10.6%
i 30828
10.6%
c 30828
10.6%
v 19511
 
6.7%
a 18608
 
6.4%
g 18608
 
6.4%
o 11317
 
3.9%
w 11317
 
3.9%
Uppercase Letter
ValueCountFrequency (%)
A 18608
60.4%
L 11317
36.7%
O 903
 
2.9%
Space Separator
ValueCountFrequency (%)
30828
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 322862
91.3%
Common 30828
 
8.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 68947
21.4%
r 50339
15.6%
p 30828
9.5%
i 30828
9.5%
c 30828
9.5%
v 19511
 
6.0%
A 18608
 
5.8%
a 18608
 
5.8%
g 18608
 
5.8%
L 11317
 
3.5%
Other values (4) 24440
 
7.6%
Common
ValueCountFrequency (%)
30828
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 353690
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 68947
19.5%
r 50339
14.2%
30828
8.7%
p 30828
8.7%
i 30828
8.7%
c 30828
8.7%
v 19511
 
5.5%
A 18608
 
5.3%
a 18608
 
5.3%
g 18608
 
5.3%
Other values (5) 35757
10.1%

Length_of_description
Real number (ℝ)

MISSING 

Distinct300
Distinct (%)1.1%
Missing4232
Missing (%)13.7%
Infinite0
Infinite (%)0.0%
Mean266.82223
Minimum6
Maximum350
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-03-17T10:16:44.449074image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile203
Q1244
median248
Q3335
95-th percentile349
Maximum350
Range344
Interquartile range (IQR)91

Descriptive statistics

Standard deviation51.873812
Coefficient of variation (CV)0.19441338
Kurtosis1.0078702
Mean266.82223
Median Absolute Deviation (MAD)4
Skewness0.079891517
Sum7096404
Variance2690.8924
MonotonicityNot monotonic
2024-03-17T10:16:44.563083image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
250 2355
 
7.6%
249 2307
 
7.5%
248 2213
 
7.2%
247 2016
 
6.5%
246 1765
 
5.7%
245 1384
 
4.5%
350 1265
 
4.1%
244 1204
 
3.9%
349 997
 
3.2%
243 962
 
3.1%
Other values (290) 10128
32.9%
(Missing) 4232
13.7%
ValueCountFrequency (%)
6 1
 
< 0.1%
7 1
 
< 0.1%
9 4
 
< 0.1%
13 26
0.1%
14 1
 
< 0.1%
15 2
 
< 0.1%
17 1
 
< 0.1%
22 1
 
< 0.1%
23 1
 
< 0.1%
27 1
 
< 0.1%
ValueCountFrequency (%)
350 1265
4.1%
349 997
3.2%
348 885
2.9%
347 748
2.4%
346 646
2.1%
345 507
1.6%
344 434
 
1.4%
343 358
 
1.2%
342 260
 
0.8%
341 194
 
0.6%

Interactions

2024-03-17T10:16:34.491907image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:25.866076image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:26.843806image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:27.840076image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:28.865247image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:30.092739image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:31.147669image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:32.164425image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:33.222820image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:34.625904image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:26.004079image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:26.959810image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:27.958718image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:28.974279image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:30.209700image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:31.254052image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:32.270424image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:33.344792image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:34.768911image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:26.106080image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:27.061773image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:28.063716image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:29.087248image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:30.337160image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:31.356047image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:32.375421image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:33.469791image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:34.887869image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:26.214114image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:27.171189image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:28.166718image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:29.337247image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:30.460165image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:31.475052image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:32.491455image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:33.580825image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:35.039036image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:26.320078image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:27.302281image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:28.277750image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:29.448247image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:30.574405image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:31.596052image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:32.609792image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:33.685788image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:35.153382image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:26.419774image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:27.428316image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:28.385717image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:29.548278image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:30.681370image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:31.706047image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:32.718828image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:33.812788image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:35.285384image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:26.524811image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:27.531310image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:28.495716image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:29.672248image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:30.792407image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:31.819049image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:32.833822image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:33.948868image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:35.409776image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:26.633809image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:27.631280image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:28.616243image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:29.830248image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:30.898637image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:31.941048image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:32.986788image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:34.054872image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:35.543746image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:26.732775image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:27.729586image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:28.755244image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:29.964702image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:31.026670image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:32.049456image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:33.092788image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2024-03-17T10:16:34.355871image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2024-03-17T10:16:44.680078image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
idpricepub_yearnum_of_authormonth_of_saledate_of_saleyear_of_saleupdated_priceLength_of_descriptionedition_numavailabeprice_rangeeditionaffordability
id1.0000.1710.9480.134-0.0090.0460.9520.171-0.0810.1220.0740.0530.1220.070
price0.1711.0000.1700.3270.017-0.0800.1561.000-0.1080.0000.0750.8770.0000.620
pub_year0.9480.1701.0000.112-0.0570.0430.9910.170-0.0780.0690.0100.0390.0690.070
num_of_author0.1340.3270.1121.0000.011-0.0390.1060.327-0.1210.2390.0000.0180.2390.054
month_of_sale-0.0090.017-0.0570.0111.000-0.014-0.0590.0170.0100.0210.0220.0070.0210.049
date_of_sale0.046-0.0800.043-0.039-0.0141.0000.040-0.0800.0070.0050.1000.0290.0050.080
year_of_sale0.9520.1560.9910.106-0.0590.0401.0000.156-0.0720.0000.0000.0040.0000.000
updated_price0.1711.0000.1700.3270.017-0.0800.1561.000-0.1080.0000.0750.8770.0000.620
Length_of_description-0.081-0.108-0.078-0.1210.0100.007-0.072-0.1081.0000.0000.0360.0740.0000.166
edition_num0.1220.0000.0690.2390.0210.0050.0000.0000.0001.0000.0000.0001.0000.031
availabe0.0740.0750.0100.0000.0220.1000.0000.0750.0360.0001.0000.0370.0000.056
price_range0.0530.8770.0390.0180.0070.0290.0040.8770.0740.0000.0371.0000.0000.379
edition0.1220.0000.0690.2390.0210.0050.0000.0000.0001.0000.0000.0001.0000.031
affordability0.0700.6200.0700.0540.0490.0800.0000.6200.1660.0310.0560.3790.0311.000

Missing values

2024-03-17T10:16:35.820746image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-17T10:16:36.231836image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-03-17T10:16:36.495331image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Titleidpriceauthorpublisherpub_yearedition_numdescriptionavailabesale_dateshort_pubnum_of_authorwidthheightprocessed_titleprocessed_descriptionprice_rangemonth_of_saledate_of_saleyear_of_saleupdated_priceeditionaffordabilityLength_of_description
0Learning Go21119036771.990000Jon BodnerO'Reilly Media2024(2nd ed.)Go has rapidly become the preferred language for building web services. Plenty of tutorials are available to teach Go's syntax to developers with experience in other programming languages, but tutorials aren't enough. They don't teach Go's idioms, so developers end up recreating patterns that don't make sense in a Go context. This practical...True2024-01-10Jan 2024197150learning gogo rapidly become preferred language building web services plenty tutorials available teach go syntax developers experience programming languages tutorials enough teach go idioms developers end recreating patterns make sense go context practical0-100110202471.992Average price345.0
1Tidy First?21112782242.990000Kent BeckO'Reilly Media2023NaNTidying up messy software is a must. And that means breaking up the code to make it more readable, and using guard clauses and helping functions to make it understandable. In this practical guide, author Kent Beck, creator of Extreme Programming and pioneer of software patterns, suggests when and where you might apply tidyings in your code. ...True2023-10-17Oct 2023197150tidy firsttidying messy software means breaking code make readable using guard clauses helping functions make understandable practical guide author kent beck creator extreme programming pioneer software patterns suggests might apply tidyings code0-1001017202342.99NaNLow price346.0
2Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow21068172596.990000Aurélien GéronO'Reilly Media2022(3rd ed.)Through a recent series of breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This bestselling book uses concrete examples, minimal theory, and production-ready...True2022-10-04Oct 2022197150hands on machine learning with scikit learn keras and tensorflowrecent series breakthroughs deep learning boosted entire field machine learning even programmers know close nothing technology use simple efficient tools implement programs capable learning data bestselling book uses concrete examples minimal theory production ready0-100104202296.993Average price346.0
3Designing Data-Intensive Applications9572933467.990000Martin KleppmannO'Reilly Media2017NaNData is at the center of many challenges in system design today. Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What...True2017-03-16Mar 2017197150designing data intensive applicationsdata center many challenges system design today difficult issues need figured scalability consistency reliability efficiency maintainability addition overwhelming variety tools including relational databases nosql datastores stream batch processors message brokers0-100316201767.99NaNAverage price347.0
4Exam Ref MS-102 Microsoft 365 Administrator21096441953.862731Orin ThomasPearson Education2023NaNPrepare for Microsoft Exam MS-102 and help demonstrate your real-world mastery of skills and knowledge required to deploy and manage Microsoft 365 and perform Microsoft 365 tenant-level implementation and administration of cloud and hybrid...True2023-10-18Oct 2023197150exam ref ms microsoft administratorprepare microsoft exam ms help demonstrate real world mastery skills knowledge required deploy manage microsoft perform microsoft tenant level implementation administration cloud hybrid0-1001018202353.86NaNLow price242.0
5CompTIA Network+ Certification All-in-One Exam Guide, Eighth Edition (Exam N10-008)21045611480.814300Mike MeyersMcGraw Hill LLC2022(8th ed.)This up-to-date Mike Meyers exam guide delivers complete coverage of every topic on the N10-008 version of the CompTIA Network+ Certification exam Get complete coverage of all the CompTIA Network+ exam objectives inside this comprehensive resource. Created and edited by Mike Meyers, the leading expert on CompTIA certification and training, ...True2022-02-11Feb 2022297150comptia network certification all in one exam guide eighth edition examdate mike meyers exam guide delivers complete coverage every topic version comptia network certification exam complete coverage comptia network exam objectives inside comprehensive resource created edited mike meyers leading expert comptia certification training0-100211202280.818Average price347.0
6Exam Ref MD-102 Microsoft Endpoint Administrator21090163253.862731Andrew WarrenPearson Education2023NaNPrepare for Microsoft Exam MD-102—and help demonstrate your real-world mastery of the skills and knowledge required to deploy, manage, and protect modern endpoints at scale in Microsoft 365 environments. Designed for endpoint administrators, this...True2023-08-18Aug 2023297150exam ref md microsoft endpoint administratorprepare microsoft exam md help demonstrate real world mastery skills knowledge required deploy manage protect modern endpoints scale microsoft environments designed endpoint administrators0-100818202353.86NaNLow price249.0
7The Staff Engineer's Path21067014742.990000Tanya ReillyO'Reilly Media2022NaNFor years, companies have rewarded their most effective engineers with management positions. But treating management as the default path for an engineer with leadership ability doesn't serve the industry well--or the engineer. The staff engineer's path allows engineers to contribute at a high level as role models, driving big projects,...True2022-09-20Sep 2022197150the staff engineer pathyears companies rewarded effective engineers management positions treating management default path engineer leadership ability serve industry engineer staff engineer path allows engineers contribute high level role models driving big projects0-100920202242.99NaNLow price340.0
8Learning Web Design9626010367.990000Jennifer RobbinsO'Reilly Media2018(5th ed.)Do you want to build web pages but have no prior experience? This friendly guide is the perfect place to start. You’ll begin at square one, learning how the web and web pages work, and then steadily build from there. By the end of the book, you’ll have the skills to create a simple site with multicolumn pages that adapt for mobile devices. Each...True2018-06-05May 2018197150learning web designwant build web pages prior experience friendly guide perfect place start begin square learning web web pages work steadily build end book skills create simple site multicolumn pages adapt mobile devices0-10065201867.995Average price350.0
9Deciphering Data Architectures21121993284.990000James SerraO'Reilly Media2024NaNData fabric, data lakehouse, and data mesh have recently appeared as viable alternatives to the modern data warehouse. These new architectures have solid benefits, but they're also surrounded by a lot of hyperbole and confusion. This practical book provides a guided tour of each architecture to help data professionals understand its pros and...True2024-02-06Feb 2024197150deciphering data architecturesdata fabric data lakehouse data mesh recently appeared viable alternatives modern data warehouse new architectures solid benefits surrounded lot hyperbole confusion practical book provides guided tour architecture help data professionals understand pros0-10026202484.99NaNAverage price346.0
Titleidpriceauthorpublisherpub_yearedition_numdescriptionavailabesale_dateshort_pubnum_of_authorwidthheightprocessed_titleprocessed_descriptionprice_rangemonth_of_saledate_of_saleyear_of_saleupdated_priceeditionaffordabilityLength_of_description
39972Conceptual Structures: Common Semantics for Sharing Knowledge247490172.641169Frithjof DauSpringer Berlin Heidelberg2005NaNNaNTrue2005-07-11Jul 2005397150conceptual structures common semantics for sharing knowledgenan0-100711200572.64NaNAverage priceNaN
39973Learning Theory2474907130.765729Peter AuerSpringer Berlin Heidelberg2005NaNThis volume contains papers presented at the Eighteenth Annual Conference on Learning Theory (previously known as the Conference on Computational Learning Theory) held in Bertinoro, Italy from June 27 to 30, 2005. The technical program contained...True2005-06-28Jun 2005297150learning theoryvolume contains papers presented eighteenth annual conference learning theory previously known conference computational learning theory held bertinoro italy june technical program contained101-5006282005130.77NaNAverage price248.0
39974Advances in Knowledge Discovery and Data Mining2474931130.765729Tu Bao HoSpringer Berlin Heidelberg2005NaNThe Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) is a leading international conference in the area of data mining and knowledge discovery. It provides an international forum for researchers and industry practitioners to...True2005-05-13May 2005397150advances in knowledge discovery and data miningpacific asia conference knowledge discovery data mining pakdd leading international conference area data mining knowledge discovery provides international forum researchers industry practitioners101-5005132005130.77NaNAverage price248.0
39975Charting the Topic Maps Research and Applications Landscape247637472.641169Lutz MaicherSpringer Berlin Heidelberg2006NaNThis book constitutes the thoroughly refereed post-proceedings of the First International Workshop on Topic Map Research and Applications, held in October 2005. The 17 revised full papers and five revised short papers presented together with one...True2006-02-15Feb 2006297150charting the topic maps research and applications landscapebook constitutes thoroughly refereed post proceedings first international workshop topic map research applications held october revised full papers five revised short papers presented together0-100215200672.64NaNAverage price248.0
39976Data Mining for Biomedical Applications247641272.641169Jinyan LiSpringer Berlin Heidelberg2006NaNThis book constitutes the refereed proceedings of the International Workshop on Data Mining for Biomedical Applications, BioDM 2006, held in Singapore in conjunction with the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining...True2006-02-28Feb 2006397150data mining for biomedical applicationsbook constitutes refereed proceedings international workshop data mining biomedical applications biodm held singapore conjunction th pacific asia conference knowledge discovery data mining0-100228200672.64NaNAverage price245.0
39977Finite-State Methods and Natural Language Processing247652772.641169Anssi Yli-JyräSpringer Berlin Heidelberg2006NaNThis book constitutes the thoroughly refereed post-proceedings of the 5th International Workshop on Finite-State Methods in Natural Language Processing, FSMNLP 2005, held in Helsinki, Finland, September 2005. The book presents 24 revised full...True2006-12-12Dec 2006397150finite state methods and natural language processingbook constitutes thoroughly refereed post proceedings th international workshop finite state methods natural language processing fsmnlp held helsinki finland september book presents revised full0-1001212200672.64NaNAverage price245.0
39978Argumentation in Multi-Agent Systems247661772.641169Simon D. ParsonsSpringer Berlin Heidelberg2006NaNNaNTrue2006-07-25Jul 2006497150argumentation in multi agent systemsnan0-100725200672.64NaNAverage priceNaN
39979PRICAI 2006: Trends in Artificial Intelligence2476718232.483709Quiang YangSpringer Berlin Heidelberg2008NaNThis book constitutes the refereed proceedings of the 9th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2006, held in Guilin, China in August 2006. The book presents 81 revised full papers and 87 revised short papers...True2008-02-20Feb 2008297150pricai trends in artificial intelligencebook constitutes refereed proceedings th pacific rim international conference artificial intelligence pricai held guilin china august book presents revised full papers revised short papers101-5002202008232.48NaNAverage price245.0
39988Knowledge Science, Engineering and Management2476736130.765729Jérôme LangSpringer Berlin Heidelberg2006NaNHere are the refereed proceedings of the First International Conference on Knowledge Science, Engineering and Management, KSEM 2006, held in Guilin, China in August 2006 in conjunction with PRICAI 2006. The book presents 51 revised full papers and...True2006-07-25Jul 2006397150knowledge science engineering and managementrefereed proceedings first international conference knowledge science engineering management ksem held guilin china august conjunction pricai book presents revised full papers101-5007252006130.77NaNAverage price250.0
39989Artificial Neural Networks in Pattern Recognition247677772.641169Friedhelm SchwenkerSpringer Berlin Heidelberg2006NaNThis book constitutes the refereed proceedings of the Second IAPR Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2006, held in Ulm, Germany in August/September 2006. The 26 revised papers presented were carefully reviewed and...True2006-08-29Aug 2006297150artificial neural networks in pattern recognitionbook constitutes refereed proceedings second iapr workshop artificial neural networks pattern recognition annpr held ulm germany august september revised papers presented carefully reviewed0-100829200672.64NaNAverage price250.0